Surgery remains the only curative option for hepatocellular carcinoma (HCC) but most affected patients have underlying liver disease that limits the extent of resection. Thus, liver transplantation has become an attractive option for treatment of HCC and several single center studies have reported excellent survival rates. In response, the US liver allocation system was changed on February 27,2002 to give a higher priority to patients with HCC meeting entry criteria employed in these studies. Because liver allocation policy is now based on assigning priority to candidates with HCC according to their estimated risk of progression beyond the favorable stages (so called "drop out' rate), data describing the rate of progression, natural history, and appropriate diagnostic' modalities, are essential for formulating this policy. The new policy and data collection instruments make it possible to analyze a cohort of patients with HCC that is an order of magnitude larger than any previously examined. The overall goal of this proposal is to exploit this very large database to provide clinicians with an improved understanding of the accuracy of pre-operative staging for HCC, the efficacy of pre-transplant ablative treatments for HCC, as well as provide policy makers with much more accurate risk models on which to base better, evidence based liver allocation policy. To address the issues of pre-operative staging accuracy, we hypothesize that MRI defined HCC stage pre-operatively in liver transplant candidates has a better correlation with histologically defined stage compared with other imaging modalities. Our specific aim is to determine which diagnostic imaging test correlates best (as measured by area under the receiver operating curve [Az]) with pathologic stage using clinically based assessment of images. To address the efficacy of pre-transplant ablative procedures, we hypothesize that HCC liver transplant candidates with presenting tumors that were clinically down staged by application of ablative treatment within a ear of transplant have patient and graft survival rates equal to candidates who had no ablative treatments and met the clinical staging inclusion criteria. Our specific aim is to determine if tumors larger than clinical stage II that are down staged by pre-transplant ablative treatments behave as the downstaged tumor size. A secondary aim of this analysis is to determine if pre-transplant ablative treatments have any effect on drop out rates and/or post-transplant survival. Finally, the recent decrease in priority for HCC candidates will be used to assess drop out rates that will inform development of refined mathematical models that more accurately predict tumor progression and drop out rates from the liver transplant waiting list. Our specific aim is to improve the calculation of waiting list priority based on the risk of tumor progression using our established Markov Model techniques to more accurately assign priority for liver transplant candidates with HCC relative to candidates with chronic liver disease. Results from this project will improve the care of patients with HCC and provide evidence for more equitably allocating the scarce donor resource. Furthermore, these analyses will serve to support future developments of prospective clinical trials.